






"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"gpt_neo"
					UpperCAmelCase__								=				["past_key_values"]
					UpperCAmelCase__								=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      Tuple=5_0_2_5_7     ,		__snake_case  :      Optional[int]=2_0_4_8     ,		__snake_case  :      List[str]=2_0_4_8     ,		__snake_case  :      List[Any]=2_4     ,		__snake_case  :      List[str]=[[["global", "local"], 1_2]]     ,		__snake_case  :      Union[str, Any]=1_6     ,		__snake_case  :      str=None     ,		__snake_case  :      List[Any]=2_5_6     ,		__snake_case  :      Any="gelu_new"     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      Optional[Any]=0.0     ,		__snake_case  :      List[Any]=0.0     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      Optional[Any]=1E-5     ,		__snake_case  :      Optional[int]=0.02     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Any=5_0_2_5_6     ,		__snake_case  :      str=5_0_2_5_6     ,		**__snake_case  :      Any     ,		)			->							Union[str, Any]:
											__magic_name__:	Union[str, Any]							=       vocab_size
											__magic_name__:	List[str]							=       max_position_embeddings
											__magic_name__:	List[str]							=       hidden_size
											__magic_name__:	List[Any]							=       num_layers
											__magic_name__:	Dict							=       num_heads
											__magic_name__:	int							=       intermediate_size
											__magic_name__:	Tuple							=       window_size
											__magic_name__:	List[str]							=       activation_function
											__magic_name__:	List[str]							=       resid_dropout
											__magic_name__:	List[Any]							=       embed_dropout
											__magic_name__:	Any							=       attention_dropout
											__magic_name__:	int							=       classifier_dropout
											__magic_name__:	Any							=       layer_norm_epsilon
											__magic_name__:	Tuple							=       initializer_range
											__magic_name__:	Any							=       use_cache

											__magic_name__:	Any							=       bos_token_id
											__magic_name__:	int							=       eos_token_id

											__magic_name__:	Optional[Any]							=       attention_types
											__magic_name__:	List[Any]							=       self.expand_attention_types_params(__snake_case					)

											if len(self.attention_layers					) != self.num_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.attention_layers)` == `config.num_layers` """
																	    F'but is `len(config.attention_layers) = {len(self.attention_layers					)}`, '
																	    F'`config.num_layers = {self.num_layers}`. '
																	    """`config.attention_layers` is prepared using `config.attention_types`. """
																	    """Please verify the value of `config.attention_types` argument."""					)

											super().__init__(bos_token_id=__snake_case     ,		eos_token_id=__snake_case     ,		**__snake_case					)






					@staticmethod
					def   lowerCamelCase__   (			__snake_case  :      Optional[Any]					)			->							Any:
											__magic_name__:	List[Any]							=       []
											for item in attention_types:
																	for _ in range(item[1]					):
																							attentions.extend(item[0]					)
											return attentions



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Tuple  )    ->							Dict:
						import torch

						__magic_name__:	List[str]							=       input.size()
						__magic_name__:	Dict							=       len(__UpperCAmelCase  )
						__magic_name__:	Tuple							=       shape[dimension]

						__magic_name__:	Tuple							=       torch.arange(0      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       torch.div(sizedim - size      ,   __UpperCAmelCase      ,   rounding_mode="""floor"""  ) + 1
						__magic_name__:	Optional[int]							=       torch.arange(__UpperCAmelCase  ) + low_indices[:min_length][:, None]

						__magic_name__:	Optional[int]							=       [slice(__UpperCAmelCase  )] * rank
						__magic_name__:	str							=       indices
						__magic_name__:	Optional[Any]							=       input[s]

						__magic_name__:	List[str]							=       list(range(0      ,   rank + 1  )  )
						perm.append(perm.pop(dimension + 1  )  )

						return sliced.permute(__UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    List[str]  )    ->							List[str]:
						import torch

						__magic_name__:	Union[str, Any]							=       torch.arange(1      ,   __UpperCAmelCase  )
						__magic_name__:	str							=       torch.remainder(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	List[str]							=       remainders == 0
						__magic_name__:	Any							=       candidates[divisor_indices]
						__magic_name__:	str							=       torch.max(__UpperCAmelCase  )
						return largest_divisor, torch.div(__UpperCAmelCase      ,   __UpperCAmelCase      ,   rounding_mode="""floor"""  )



class       __A		(   SCREAMING_SNAKE_CASE_			):

					@property
					def   lowerCamelCase__   (			self  :      str					)			->							Mapping[str, Mapping[int, str]]:
											__magic_name__:	Union[str, Any]							=       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}					)
											if self.use_past:
																	self.fill_with_past_key_values_(__snake_case     ,		direction="""inputs"""					)
																	__magic_name__:	Union[str, Any]							=       {0: """batch""", 1: """past_sequence + sequence"""}
											else:
																	__magic_name__:	List[str]							=       {0: """batch""", 1: """sequence"""}

											return common_inputs

					@property
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											return self._config.num_heads

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      PreTrainedTokenizer     ,		__snake_case  :      int = -1     ,		__snake_case  :      int = -1     ,		__snake_case  :      bool = False     ,		__snake_case  :      Optional[TensorType] = None     ,		)			->							Mapping[str, Any]:
											__magic_name__:	Dict							=       super(__snake_case     ,		self					).generate_dummy_inputs(
											    __snake_case     ,		batch_size=__snake_case     ,		seq_length=__snake_case     ,		is_pair=__snake_case     ,		framework=__snake_case					)

											# We need to order the input in the way they appears in the forward()
											__magic_name__:	Optional[Any]							=       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}					)

											# Need to add the past_keys
											if self.use_past:
																	if not is_torch_available():
																							raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""					)
																	else:
																							import torch

																							__magic_name__,			__magic_name__:	Tuple							=       common_inputs["""input_ids"""].shape
																							# Not using the same length for past_key_values
																							__magic_name__:	Optional[int]							=       seqlen + 2
																							__magic_name__:	Union[str, Any]							=       (
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							__magic_name__:	int							=       [
																							    (torch.zeros(__snake_case					), torch.zeros(__snake_case					)) for _ in range(self.num_layers					)
																							]

											__magic_name__:	Union[str, Any]							=       common_inputs["""attention_mask"""]
											if self.use_past:
																	__magic_name__:	Optional[Any]							=       ordered_inputs["""attention_mask"""].dtype
																	__magic_name__:	Union[str, Any]							=       torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(__snake_case     ,		__snake_case     ,		dtype=__snake_case					)]     ,		dim=1					)

											return ordered_inputs






					@property
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											return 1_3




def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				len(lowercase				)
				__lowercase					     =				len(lowercase				)
				__lowercase					     =				[[False for _ in range(m + 1				)] for _ in range(n + 1				)]
				__lowercase					     =				True
				for i in range(lowercase				):
								for j in range(m + 1				):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				__lowercase					     =				True
																if a[i].islower():
																				__lowercase					     =				True
				return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()